Image segmentation is an important component of image processing which is necessary in the early stages of image analysis. Typical methods of image segmentation are utilizing region information. They use statistics, such as the mean and standard deviation of the pixel intensity within sub-images, with the final segmentation being obtained by a succession of splitting and merging processes of sub-images in order to create regions with quasi- homogeneous properties. In this paper, we propose a co- occurrence matrix based method of image segmentation in region-based techniques. It utilizes the observation that features of the multiple windows neighboring a pixel do not differ significantly from one another, and that features corresponding to pixels belonging to the same object form a cluster in the feature space, which may frequently be approximated by a Gaussian distribution. This paper extends the co-occurrence matrix based method. The definition of co- occurrence features is extended from one dimension to many dimensions: the number of observation windows is extended from two to an arbitrary number.
After Cataract surgery where a plastic implant lens is implanted into the eye to replace the natural lens, many patients suffer from cell growth across a membrane situated at the back of the lens which degrades their vision. The cell growth is known as Posterior Capsule Opacification (or PCO). It is important to be able to quantify PCO so that the effect of different implant lens types and surgical techniques may be evaluated. Initial results obtained using a neural network to detect PCO from implant lenses are compared to an established but less automated method of detection, which segments the images using texture segmentation in conjunction with co- occurrence matrices. Tests show that the established method performs well in clinical validation and repeatability trials. The requirement to use a neural network to analyze the implant lens images evolved from the analysis of over 1000 images using the established co-occurrence matrix segmentation method. The work shows that a method based on neural networks is a promising tool to automate the procedure of calculating PCO.
We present further improvements to the methods of interpretation of the Posterior Capsular Opacification (PCO) images. These retro-illumination images of the back surface of the implanted lens are used to monitor the state of patient's vision after cataract operation. A common post-surgical complication is opacification of the posterior eye capsule caused by the growth of epithelial cells across the back surface of the capsule. Interpretation of the PCO images is based on their segmentation into transparent image areas and opaque areas, which are affected by the growth of epithelial cells and can be characterized by the increase in the image local variance. This assumption is valid in majority of cases. However, for different materials used for the implanted lenses it sometimes happens that the epithelial cells grow in a way characterized by low variance. In such a case segmentation gives a relatively big error. We describe an application of an anisotropic diffusion equation in a non-linear pre-processing of PCO images. The algorithm preserves the high-variance areas of PCO images and performs a low-pass filtering of small low- variance features. The algorithm maintains a mean value of the variance and guarantees existence of a stable solution and improves segmentation of the PCO images.
This paper presents new techniques for the texture classification of regions based on edge co- occurrence matrices and discrete Hermite functions which are used to describe them. The paper briefly defines co-occurrence matrices and how they can be used to describe the relationship of edges around a pixel. Texture is interpreted as a measure of the edginess about a pixel and is described by edge co-occurrence matrices. The texture of the region is characterized by an orthogonal decomposition of the co-occurrence matrix using 2-dimensional discrete Hermite functions. The coefficients of this decomposition provide a low order feature vector which can be used for texture classification. The coefficients of the Hermite functions used in the decomposition of the co-occurrence matrix are analyzed by two neural network classifiers: the multilayer perceptron and the cascade correlation. Experiments have been performed for the training and validation of the networks on two types of terrain (grass and trees) taken from FLIR images during a low level approach to a bridge.
Using navigational data, algorithms are defined for atmospheric absorption compensation by range dependent contrast enhancement, debanding by sensor response equalisation, and signal to noise enhancement by summation of image sub-sequences. Improved signal to noise is demonstrated by application to time sequences of infra-red images. Registration accuracy is examined as a function of range.
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